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		      	<h2 class="project-tagline">Stanford University, Winter 2023</h2>
				
				
				
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					<p>This course provides in-depth coverage of the architectural techniques used to design accelerators for training and inference in machine learning systems. This course will cover classical ML algorithms such as linear regression and support vector machines as well as DNN models such as convolutional neural nets, and recurrent neural nets. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. We will cover the design of accelerators for ML model inference and training. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. To design energy-efficient accelerators, students will develop the intuition to make trade-offs between ML model parameters and hardware implementation techniques. Students will read recent research papers and complete a design project.<p>
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				<h2>Instructors and office hours:<h2>
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             <a href="http://arsenalfc.stanford.edu/kunle">
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              <div class="text-white">Kunle Olukotun</div>
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            <a href="https://web.stanford.edu/~perdavan/">
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              <div class="text-white">Ardavan Pedram</div>
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					 <div class="text-white">Office Hours TBA</div>
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	        			<div class="text-white">Vardan Papyan</div>
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					<div class="text-white">Tue 2PM in Sequoia 208</div>
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